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Ahesui
by Ahesui

Jev candidate ranker

jev_rank
Read-onlyIdempotent

Rank up to 5,000 files, symbols, errors, or skills against a plain-language query. Verifies the top match actually answers the query, even for large candidate lists.

Instructions

Rank files, symbols, errors, or skills against a plain-language query. No embeddings. One Choice over candidate ids plus a Noul that the top hit actually answers the query (so a forced winner cannot masquerade as a match). Accepts up to 5,000 supplied candidates; each Jev call uses at most 250 options and larger lists are chunked then re-ranked. Pass candidates in; this server does not index the repo.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
queryYesWhat you are looking for, in natural language
top_kNoHow many ranked candidates to return. Default 5.
candidatesYesCandidates with unique IDs. Large lists are ranked in batches that fit the context budget; at most 5000 candidates are accepted per request.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
topYes
modelYes
usageYes
actionYes
chunksNo
existsYes
winnerYes
chunkedYes
coverageYes
truncatedYes
exists_verdictYes
winner_confidenceYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnly, openWorld, idempotent), the description discloses the Noul mechanism that verifies the top hit actually answers the query, the chunking behavior (each call uses at most 250 options, larger lists are chunked and re-ranked), and the 5,000-candidate limit. These are valuable behavioral traits not captured in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences with no fluff. It front-loads the primary purpose, then adds key behavioral details, and ends with a scope disclaimer. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists, return values are covered. The description explains the core ranking behavior, the Noul check, chunking, candidate limits, and the server's non-indexing scope. This is sufficient for an agent to correctly invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 75%; query, top_k, and candidates have descriptions. The description adds context about candidate limits and chunking but does not explain the 'model' parameter or further clarify top_k semantics beyond the schema. It adds some value but does not fully compensate for the missing model description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the exact verb 'Rank' and the resource types ('files, symbols, errors, or skills') against a plain-language query. It also differentiates by noting 'No embeddings' and 'Pass candidates in; this server does not index the repo,' making its scope distinct from repo-indexing tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: it requires supplied candidates and does not index the repo, implying it should be used when candidates are already available. It does not explicitly name alternative tools or list when-not conditions beyond 'does not index the repo,' so it lacks explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.